Tailored interventions to improve hypertension management after stroke or TIA--phase II (TIMS II).
Bibliographic record
Abstract
BACKGROUND: Reduction of blood pressure (BP) after stroke or TIA decreases stroke recurrence and is a major goal ofsecondary Stroke Prevention Clinics (SPCs). Health care providers need effective screening processes to identify those clients at highest risk of not achieving BP targets and those clients at highest risk ofnon-adherence to medication. METHODS: This multicentred, randomized controlled study used a screening process to identify SPC patients with psychosocial/cognitive deficits (e.g., lack of confidence in the utility of medications, poor memory, mild cognitive impairment) who were experiencing difficulty managing their BP to target values and evaluated whether a model of nurse-led case management program (monthly telephone calls, motivational interviewingfor lifestyle change, plus home BP monitoring and use ofdosettes for medication administration) would improve BP measures and adherence to medications. RESULTS: Both intervention (n=29) and usual care groups (n=27) showed a trend-for'reduced BP at six months (Median ql-q3, Systolic BR p=0.46; Diastolic BR p=0.37). Diabetic patients, irrespective of the group to which they were randomized, were less likely to meet Best Practice Guideline targets than those without diabetes (Chi Square test, p=0.0001). CONCLUSION: Stroke and TIA patients with diabetes may require additional resources and support in order to reach BP target values.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".